#ʟɢᴍᴠɪᴩᴏᴄᴛᴏʙᴇʀ @ LetsGrowMore

Author: Tushar Jain

To Perform: As a "Security/Defense Analyst" find out the hot zone of "Global Terrorism"

Task 2

DATASET : https://bit.ly/2TK5Xn5

Let us import all the libraries required for the task

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os
import warnings
import plotly.express as px
warnings.filterwarnings("ignore")

Importing the dataset

data=pd.read_csv("globalterrorismdb_0718dist.csv" ,encoding='latin1')
data.head()

	eventid	iyear	imonth	iday	approxdate	extended	resolution	country	country_txt	region	...	addnotes	scite1	scite2	scite3	dbsource	INT_LOG	INT_IDEO	INT_MISC	INT_ANY	related
0	197000000001	1970	7	2	NaN	0	NaN	58	Dominican Republic	2	...	NaN	NaN	NaN	NaN	PGIS	0	0	0	0	NaN
1	197000000002	1970	0	0	NaN	0	NaN	130	Mexico	1	...	NaN	NaN	NaN	NaN	PGIS	0	1	1	1	NaN
2	197001000001	1970	1	0	NaN	0	NaN	160	Philippines	5	...	NaN	NaN	NaN	NaN	PGIS	-9	-9	1	1	NaN
3	197001000002	1970	1	0	NaN	0	NaN	78	Greece	8	...	NaN	NaN	NaN	NaN	PGIS	-9	-9	1	1	NaN
4	197001000003	1970	1	0	NaN	0	NaN	101	Japan	4	...	NaN	NaN	NaN	NaN	PGIS	-9	-9	1	1	NaN

5 rows × 135 columns

data.tail()

	eventid	iyear	imonth	iday	approxdate	extended	resolution	country	country_txt	region	...	addnotes	scite1	scite2	scite3	dbsource	INT_LOG	INT_IDEO	INT_MISC	INT_ANY	related
181686	201712310022	2017	12	31	NaN	0	NaN	182	Somalia	11	...	NaN	"Somalia: Al-Shabaab Militants Attack Army Che...	"Highlights: Somalia Daily Media Highlights 2 ...	"Highlights: Somalia Daily Media Highlights 1 ...	START Primary Collection	0	0	0	0	NaN
181687	201712310029	2017	12	31	NaN	0	NaN	200	Syria	10	...	NaN	"Putin's 'victory' in Syria has turned into a ...	"Two Russian soldiers killed at Hmeymim base i...	"Two Russian servicemen killed in Syria mortar...	START Primary Collection	-9	-9	1	1	NaN
181688	201712310030	2017	12	31	NaN	0	NaN	160	Philippines	5	...	NaN	"Maguindanao clashes trap tribe members," Phil...	NaN	NaN	START Primary Collection	0	0	0	0	NaN
181689	201712310031	2017	12	31	NaN	0	NaN	92	India	6	...	NaN	"Trader escapes grenade attack in Imphal," Bus...	NaN	NaN	START Primary Collection	-9	-9	0	-9	NaN
181690	201712310032	2017	12	31	NaN	0	NaN	160	Philippines	5	...	NaN	"Security tightened in Cotabato following IED ...	"Security tightened in Cotabato City," Manila ...	NaN	START Primary Collection	-9	-9	0	-9	NaN

5 rows × 135 columns

data.shape

(181691, 135)

data.dtypes

eventid        int64
iyear          int64
imonth         int64
iday           int64
approxdate    object
               ...  
INT_LOG        int64
INT_IDEO       int64
INT_MISC       int64
INT_ANY        int64
related       object
Length: 135, dtype: object

data.index

RangeIndex(start=0, stop=181691, step=1)

data.size

24528285

data.columns

Index(['eventid', 'iyear', 'imonth', 'iday', 'approxdate', 'extended',
       'resolution', 'country', 'country_txt', 'region',
       ...
       'addnotes', 'scite1', 'scite2', 'scite3', 'dbsource', 'INT_LOG',
       'INT_IDEO', 'INT_MISC', 'INT_ANY', 'related'],
      dtype='object', length=135)

data.isnull().sum()

eventid            0
iyear              0
imonth             0
iday               0
approxdate    172452
               ...  
INT_LOG            0
INT_IDEO           0
INT_MISC           0
INT_ANY            0
related       156653
Length: 135, dtype: int64

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 181691 entries, 0 to 181690
Columns: 135 entries, eventid to related
dtypes: float64(55), int64(22), object(58)
memory usage: 187.1+ MB

data.describe()

	eventid	iyear	imonth	iday	extended	country	region	latitude	longitude	specificity	...	ransomamt	ransomamtus	ransompaid	ransompaidus	hostkidoutcome	nreleased	INT_LOG	INT_IDEO	INT_MISC	INT_ANY
count	1.816910e+05	181691.000000	181691.000000	181691.000000	181691.000000	181691.000000	181691.000000	177135.000000	1.771340e+05	181685.000000	...	1.350000e+03	5.630000e+02	7.740000e+02	552.000000	10991.000000	10400.000000	181691.000000	181691.000000	181691.000000	181691.000000
mean	2.002705e+11	2002.638997	6.467277	15.505644	0.045346	131.968501	7.160938	23.498343	-4.586957e+02	1.451452	...	3.172530e+06	5.784865e+05	7.179437e+05	240.378623	4.629242	-29.018269	-4.543731	-4.464398	0.090010	-3.945952
std	1.325957e+09	13.259430	3.388303	8.814045	0.208063	112.414535	2.933408	18.569242	2.047790e+05	0.995430	...	3.021157e+07	7.077924e+06	1.014392e+07	2940.967293	2.035360	65.720119	4.543547	4.637152	0.568457	4.691325
min	1.970000e+11	1970.000000	0.000000	0.000000	0.000000	4.000000	1.000000	-53.154613	-8.618590e+07	1.000000	...	-9.900000e+01	-9.900000e+01	-9.900000e+01	-99.000000	1.000000	-99.000000	-9.000000	-9.000000	-9.000000	-9.000000
25%	1.991021e+11	1991.000000	4.000000	8.000000	0.000000	78.000000	5.000000	11.510046	4.545640e+00	1.000000	...	0.000000e+00	0.000000e+00	-9.900000e+01	0.000000	2.000000	-99.000000	-9.000000	-9.000000	0.000000	-9.000000
50%	2.009022e+11	2009.000000	6.000000	15.000000	0.000000	98.000000	6.000000	31.467463	4.324651e+01	1.000000	...	1.500000e+04	0.000000e+00	0.000000e+00	0.000000	4.000000	0.000000	-9.000000	-9.000000	0.000000	0.000000
75%	2.014081e+11	2014.000000	9.000000	23.000000	0.000000	160.000000	10.000000	34.685087	6.871033e+01	1.000000	...	4.000000e+05	0.000000e+00	1.273412e+03	0.000000	7.000000	1.000000	0.000000	0.000000	0.000000	0.000000
max	2.017123e+11	2017.000000	12.000000	31.000000	1.000000	1004.000000	12.000000	74.633553	1.793667e+02	5.000000	...	1.000000e+09	1.320000e+08	2.750000e+08	48000.000000	7.000000	2769.000000	1.000000	1.000000	1.000000	1.000000

8 rows × 77 columns

data.rename(columns={'iyear':'Year','imonth':'Month','iday':"day",'gname':'Group','country_txt':'Country','region_txt':'Region','provstate':'State','city':'City','latitude':'latitude',
    'longitude':'longitude','summary':'summary','attacktype1_txt':'Attacktype','targtype1_txt':'Targettype','weaptype1_txt':'Weapon','nkill':'kill',
     'nwound':'Wound'},inplace=True)

data = data[['Year','Month','day','Country','State','Region','City','latitude','longitude',"Attacktype",'kill',
               'Wound','target1','summary','Group','Targettype','Weapon','motive']]

Filling NA values with zero

data['Wound'] = data['Wound'].fillna(0)
data['kill'] = data['kill'].fillna(0)

data['Casulty'] = data['Wound'] + data['kill']

data.head()

	Year	Month	day	Country	State	Region	City	latitude	longitude	Attacktype	kill	Wound	target1	summary	Group	Targettype	Weapon	motive	Casulty
0	1970	7	2	Dominican Republic	NaN	Central America & Caribbean	Santo Domingo	18.456792	-69.951164	Assassination	1.0	0.0	Julio Guzman	NaN	MANO-D	Private Citizens & Property	Unknown	NaN	1.0
1	1970	0	0	Mexico	Federal	North America	Mexico city	19.371887	-99.086624	Hostage Taking (Kidnapping)	0.0	0.0	Nadine Chaval, daughter	NaN	23rd of September Communist League	Government (Diplomatic)	Unknown	NaN	0.0
2	1970	1	0	Philippines	Tarlac	Southeast Asia	Unknown	15.478598	120.599741	Assassination	1.0	0.0	Employee	NaN	Unknown	Journalists & Media	Unknown	NaN	1.0
3	1970	1	0	Greece	Attica	Western Europe	Athens	37.997490	23.762728	Bombing/Explosion	0.0	0.0	U.S. Embassy	NaN	Unknown	Government (Diplomatic)	Explosives	NaN	0.0
4	1970	1	0	Japan	Fukouka	East Asia	Fukouka	33.580412	130.396361	Facility/Infrastructure Attack	0.0	0.0	U.S. Consulate	NaN	Unknown	Government (Diplomatic)	Incendiary	NaN	0.0

Visualizing terrorist activities by region each year

px.scatter_mapbox(data, lat="latitude",
                  lon="longitude",
                  color="Wound",
                  size="Casulty",
                  size_max=20, zoom=2,
                  mapbox_style="carto-positron")

pd.crosstab(data.Year, data.Region).plot(kind='area',stacked=False,figsize=(20,10))
plt.title('Terrorist Activities By Region In Each Year',fontsize=25)
plt.ylabel('Number of Attacks',fontsize=20)
plt.xlabel("Year",fontsize=20)
plt.show()

px.scatter(data, x="kill", y="Wound",
           animation_frame="Year",
           animation_group="Country",
           size="Year",
           color="Country",
           hover_name="Country",
           log_x=True, size_max=45, range_x=[1,50], range_y=[1,50])

Visualizing the attacks from the year 1970 to 2017

year = data['Year'].unique()
years_count = data['Year'].value_counts(dropna = False).sort_index()
plt.figure(figsize = (18,10))
sns.barplot(x = year,
           y = years_count,
           palette = "tab10")
plt.xticks(rotation = 50)
plt.xlabel('Attacking Year',fontsize=20)
plt.ylabel('Number of Attacks Each Year',fontsize=20)
plt.title('Attacks In Years',fontsize=25)
plt.show()

Top 10 countries with most number of attacks

attack = data.Country.value_counts()[:10]
attack

Iraq              24636
Pakistan          14368
Afghanistan       12731
India             11960
Colombia           8306
Philippines        6908
Peru               6096
El Salvador        5320
United Kingdom     5235
Turkey             4292
Name: Country, dtype: int64

Top 10 terrorist groups with most number of attacks

data.Group.value_counts()[1:11]

Taliban                                             7478
Islamic State of Iraq and the Levant (ISIL)         5613
Shining Path (SL)                                   4555
Farabundo Marti National Liberation Front (FMLN)    3351
Al-Shabaab                                          3288
New People's Army (NPA)                             2772
Irish Republican Army (IRA)                         2671
Revolutionary Armed Forces of Colombia (FARC)       2487
Boko Haram                                          2418
Kurdistan Workers' Party (PKK)                      2310
Name: Group, dtype: int64

Here we shall observe top countries affected

plt.subplots(figsize=(20,10))
sns.barplot(data['Country'].value_counts()[:10].index,data['Country'].value_counts()[:10].values,palette='rocket')
plt.title('Top Countries Affected', fontsize=25)
plt.xlabel('Countries', fontsize=20)
plt.ylabel('Count', fontsize=20)
plt.xticks(rotation = 50)
plt.show()

Visualizing deaths due to terror attack

data1 = data[['Year','kill']].groupby(['Year']).sum()
fig, ax4 = plt.subplots(figsize=(20,10))
data1.plot(kind='bar',alpha=0.7,ax=ax4)
plt.xticks(rotation = 50)
plt.title("People Died Due To Attack",fontsize=25)
plt.ylabel("Number of killed peope",fontsize=20)
plt.xlabel('Year',fontsize=20)
top_side = ax4.spines["top"]
top_side.set_visible(False)
right_side = ax4.spines["right"]
right_side.set_visible(False)

Here is the chart for the total deaths/year due to attacks

Top 10 most effected cities are given below in the graph

data['City'].value_counts().to_frame().sort_values('City',axis=0,ascending=False).head(10).plot(kind='bar',figsize=(20,10),color='red')
plt.xticks(rotation = 50)
plt.xlabel("City",fontsize=20)
plt.ylabel("Number of attack",fontsize=20)
plt.title("Top 10 most effected city",fontsize=25)
plt.show()

Here we visualise different types of the terrorist attack

px.scatter_3d(data, x='Attacktype', y='kill', z='Wound', color='Casulty', title="Number of killed and Wounded")

Visualizing types of Terrorist attacks

plt.subplots(figsize=(20,10))
sns.countplot(data["Targettype"],order=data['Targettype'].value_counts().index,palette="gist_heat",edgecolor=sns.color_palette("mako"));
plt.xticks(rotation=90)
plt.xlabel("Attacktype",fontsize=20)
plt.ylabel("count",fontsize=20)
plt.title("Type of attack",fontsize=25)
plt.show()

Top 10 terrorist group name

data['Group'].value_counts().to_frame().drop('Unknown').head(10).plot(kind='bar',color='yellow',figsize=(20,10))
plt.title("Top 10 terrorist group attack",fontsize=25)
plt.xlabel("terrorist group name",fontsize=20)
plt.ylabel("Attack number",fontsize=20)
plt.show()

Top 10 terrorist group active in countries and number of people killed by them.

data1=data[['Group','Country','kill']]
data1=data1.groupby(['Group','Country'],axis=0).sum().sort_values('kill',ascending=False).drop('Unknown').reset_index().head(10)
data1

	Group	Country	kill
0	Islamic State of Iraq and the Levant (ISIL)	Iraq	31058.0
1	Taliban	Afghanistan	29269.0
2	Boko Haram	Nigeria	16917.0
3	Shining Path (SL)	Peru	11595.0
4	Liberation Tigers of Tamil Eelam (LTTE)	Sri Lanka	10928.0
5	Al-Shabaab	Somalia	8176.0
6	Farabundo Marti National Liberation Front (FMLN)	El Salvador	8019.0
7	Islamic State of Iraq and the Levant (ISIL)	Syria	6883.0
8	Nicaraguan Democratic Force (FDN)	Nicaragua	6630.0
9	Tehrik-i-Taliban Pakistan (TTP)	Pakistan	6014.0

Conclusions:

1.The most numbers of attacks was done by : Taliban

2.The most affected country with 24636 attacks is : Iraq

3.Most Number of attacks were done in the year : 2014

4.The most affected region is : Middle-East and north Africa

5.The most affected countries are : Iraq is the most affected country followed by Pakistan, Afghanistan and India

6.The badly affected by the terrorist attacks : Baghdad and Karachi

7.Attacktype with most wounded : Bombing/Explosion

8.Major Terrorist group : ISIL

Thank You😊